The abundance of the potential pathogen Staphylococcus hominis in the air microbiome in a dental clinic and its susceptibility to far‐UVC light

Abstract The dental clinic air microbiome incorporates microbes from the oral cavity and upper respiratory tract (URT). This study aimed to establish a reliable methodology for air sampling in a dental clinic setting and quantify the abundance of culturable mesophilic aerobic bacteria present in these samples using regression modeling. Staphylococcus hominis, a potentially pathogenic bacterium typically found in the human oropharynx and URT, was consistently isolated. S. hominis was the most abundant species of aerobic bacteria (22%–24%) and comprised 60%–80% of all Staphylococcus spp. The study also assessed the susceptibility of S. hominis to 222 nm‐far‐UVC light in laboratory experiments, which showed an exponential surface inactivation constant of k = 0.475 cm2/mJ. This constant is a critical parameter for future on‐site use of far‐UVC light as a technique for reducing pathogenic bacterial load in dental clinics.

pathogenic bacteria to other patients who are undertaking dental treatment in this setting (Kimmerle et al., 2012).
The indoor air microbiome of dental clinics is understudied and data on the potentially high loads of oral pathogens being significant contributors is not readily available in the literature. It has been reported that periodontal infections are mostly caused by nonsporulating obligate anaerobic bacteria, such as Fusobacterium nucleatum (Gram-negative) Prevotella intermedia (Gramnegative), Actinomyces israelii (Gram-positive) and facultative anaerobic Gram-positive cocci such as Streptococcus sanguinis and other oropharyngeal Streptococcus Anginosus Group (SAG) being the most common isolates. Although these bacterial species are considered to be a normal part of the microbiota of the oral cavity and gastrointestinal tract, they have also been reported to cause infections and systemic diseases (Han et al., 2000;Jiang et al., 2020;Martini et al., 2020). For example, SAG bacteria have been isolated from the sputa of patients with cystic fibrosis, chronic obstructive pulmonary disease, and bronchiectasis (Waite et al., 2012). The significance of this study is to find out if there are pathogenic bacteria, such as SAG bacteria, in the dental clinic air microbiome which could be transmitted by air and therefore be a contributing factor to respiratory infections in immunocompromised or elderly patients who are undergoing dental care at the clinic.
Staphylococcus epidermidis is one of the most abundant bacteria in the nasal cavity and can prevent the colonization of the nostrils with respiratory pathogens (Ortega-Peña et al., 2022). It is also reported in the literature that Staphylococcus hominis is the second most frequently isolated Coagulase-Negative Staphylococcus species (CoNS) from healthy skin, and there is emerging evidence to suggest that it may play a significant role in excluding pathogens, including Staphylococcus aureus, from colonizing or infecting the skin (Severn et al., 2022).
This study aimed to establish a protocol for sampling indoor air in a dental clinic and assessing the diversity of the air microbiome. This study aimed to use its findings to predict the makeup of the air microbial environment in the clinic using regression modeling and statistical analysis. The focus was on a baseline for the most abundant culturable aerobic microbial species with relevance to the oral microbiology of the oropharynx and URT, which are potentially transmitted through the air. The susceptibility of the most abundant of the isolated bacterial species to 222 nm-far-UVC light was also assessed in laboratory experiments, to use far-UVC light to reduce the number of those potentially pathogenic bacteria found in the dental clinic in future studies.

| Active samplings and microbial culturing
Air samples were collected at two distinct locations within the Vanderbilt Dental Clinic at the Columbia University College of Dental Medicine, New York City, NY. This dental clinic used has approximately 4300 ft 2 , and a 10 ft high ceiling. The total number of people in the room was counted every 30 min, that is, twice an hour throughout the total period in which the air samplings were taken in the dental clinic. Although the dental clinic has two entrances, patients gain access to the treatment room using only one entrance. The maximum number of chairs for dental treatments is 24. The dentists always wore two masks, a KN95 plus a surgical mask, according to standard protocol in the dental clinic. Patients had 1-, 2-, or 3-h appointment slots, and were not wearing masks while undergoing dental treatment.
The dental clinic has a variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system for ventilation and temperature control within the space. All of the supply air diffusers and return grilles for the VAV HVAC system are located within the ceiling. The HVAC system utilizes a single 12,000 cfm air handling unit for the clinical area where sampling occurred.
The volume of air supplied for the clinic equates to approximately 10 air changes per hour (ACH). The supply is filtered using air filters with a minimum efficiency reporting a value of 14 (MERV14).
The two locations within the dental treatment area used for air samplings were over two 1.5 m in height cabinets situated in the center of the dental clinic open-space room. These locations were chosen to assess patients' microbial load contribution while they receive dental care. Samples were taken during a period of either 3 or 4 h while the clinic was open to patients and on 2 days of each week between November 2021 and March 2022. Concomitantly active air sampling and passive air sampling were used, as follows.
For the active air sampling, one Sartorius MD8 Airport was used, which was fitted with a 9 cm gelatin filter and operated at an airflow rate of 50 L/min. Air samplings were performed following this protocol in November 2021: the first filter for 15 min, a second filter for 1 h, then a third filter for 2 h; in December 2021: the first filter for 1 h, then a second filter for 3 h. Following analysis of initial results, the regime was changed to one filter for 1 h then a second filter for 3 h in one location. This later regime was followed in February and March 2022 active air samplings: the first filter for 1 h and a second filter for 3 h on each location. Table 1 summarizes all protocols used in this study.
After active sampling, the gelatin filter was placed into a 50 mL conical centrifuge tube containing 10 mL of melted sterile Trypticase Soy Agar (TSA) 0.75%, this is the soft TSA top layer in this protocol.
TSA as a rich media would promote the growth of any viable but stressed bacteria cells propelled into the air as aerosols from dental procedures, physically injured due to the airport suction or floating for too long in the dental clinic air. No antibiotics were used for plating out active sampling filters because this study intended to assess the presence of fungal spores in the air. This soft TSA had been kept in a water bath at 50°C to remain liquid. This 10 mL original stock (zero dilution) from each filter was kept in the water bath for 20 s to ensure all gelatin was dissolved.
Then, 5 mL of this stock volume was poured onto a TSA 1% agar plate (bottom layer). The remaining 5 mL were aliquoted in 1 mL portions and transferred individually to 4 mL of liquid TSA 0.75% and then poured onto TSA 1% agar plates. Those 1-mL aliquots are the 10 −1 dilution from the original stock. All plates were left to set, inverted then incubated at 37°C for observation and counting of colony-forming units (CFUs) in 72 h ( Figure 1).

| Passive samplings and microbial culturing
For the passive air sampling, 9-cm media plates with 25 mL of TSA with 50 mg/L cycloheximide were used in one or two locations in the dental clinics: 10 plates in November/December 2021 in 1 location, or 20 media plates in February/March 2022 in 2 locations. Therefore, in those last 2 months, the total number of passive media plates used in the 4 h in two locations in the dental clinic was 160 plates per day of sampling. The two locations were on the top of two cabinets of just over 1.5 m in height in the center of the dental clinic. This set-up follows the 1/1/1 scheme for passive sampling onto media plates (Pasquarella et al., 2000), that is, sampling for 1 h, at least 1 m from the floor, at least 1 m away from walls, or any obstacle.  After incubation for both active and passive samplings, CFUs were counted, and representatives of various bacterial colony morphotypes were chosen, purified, and Gram-stained.

| Regression modeling and statistical analysis
The data collected from air sampling were compiled and imported into R 4.2.0 software for regression analysis (R Core Team, 2017). The dependent variable (outcome) was bacteria_CFUs (colony counts per sample). Since this is largely a proof-of-concept study, the combined colony counts for all bacteria were used to obtain larger numbers for statistical analysis to determine the major trends in the data.
The independent (predictor) variables were sampling (active or passive), month (calendar month of samplings), weekday (Tuesday or Thursday), hour (hour of samplings, 1, 2, or 3), location (on top of cabinet A or B) and people (average number of people in the room).
The goal of the analysis was to construct a model for predicting the outcome variable (bacterial abundance) using the predictor variables.
Bacterial CFU counts were significantly associated with two predictor variables: the day of the week (Tuesday or Thursday) and the number of people present in the dental clinic during sampling. At the dental clinic, Thursdays have patients' appointments from 8 AM to 3 PM, but Tuesdays only hold patients' appointments in the morning from 8 AM to 11 AM. Tuesday afternoons are reserved only for students to learn how to make dental prostheses, and they use human-sized dummies for that. Details of the modeling procedure are described in Appendix.
In addition to the analysis of dental clinic air samples, this study also analyzed the survival data for the most abundant and relevant Staphylococcus spp. strains exposed to 222 nm-far-UVC light in laboratory experiments. An exponential dose-response model was used as described in the following equation, where S is the surviving fraction of bacterial CFUs, D is UV dose and k is the inactivation constant: This study used linear regression to estimate parameter k, fitted by a robust procedure using the rlm function in the MASS R package.
The slope of the regression line (k value) represents the sensitivity of each strain to inactivation by UVC, assuming an exponential dose response.

| Bacterial identification using 16S ribosomal RNA (rRNA) gene
After colonies were chosen from active and passive sampling media plates, they were streaked onto fresh TSA plates for purification and isolation. Colonies were picked and resuspended in 30μL water. The 27F and 1492R primers were used to amplify the 16S rRNA gene using KAPA HiFi DNA Polymerase (Frank et al., 2008;Lane, 1991).  (Wick et al., 2017a) and were filtered with mothur for a minimum length of 5000 bp (Schloss et al., 2009). Hybrid assembly was performed using processed Nanopore long reads and Illumina short reads with unicycler (v0.5.0) tool (Wick et al., 2017b).
Prokka (version 1.12) was used for open reading frame prediction and annotation of the resultant assembly (Seemann, 2014). A bandage was used to visualize de novo assembly and differentiate any chromosome versus plasmid contigs (Wick et al., 2015). Identification of mobile genetic elements was done with PHASTER and Island Viewer (Arndt et al., 2016;Bertelli et al., 2017).
After excluding mobile genetic elements and integrated phage regions, Illumina reads were mapped against the M1018 reference genome, and variant-calling was performed using Snippy (v. 4.6.0) (Seemann, 2017 Table 2. These results support the conclusion that performance using training and testing data did not differ dramatically. Such an outcome is expected because the model is quite simple (has only two predictors), so, it is unlikely to overfit the data. A potential interaction between these predictor variables was not statistically significant (p = 0.89).
In addition to bacteria, the presence of fungi in air samples was also assessed. The fungal CFU counts, which were present on the two-layer media plates without antibiotics for active sampling, were identified mostly as Penicillium spp. and Aspergillus spp. based on their typical colonial morphologies (Samsom et al., 1988). There was high variability in the fungal CFU counts, which is possibly attributed to the variable nature of the VAV HVAC system, which could vary the amount of airflow between assessment periods or days, assuming the fungal spores were loaded into the clinic air from the HVAC system.

| Air sampling protocols and bacterial identification
The in the NB modeling, and as mentioned above, the sampling time was included in the modeling as the variable "hour", but this variable was later dropped because it did not reach statistical significance.
The passive sampling allowed for a snapshot of each 1 h over the 4 h of air sampling at the dental clinic, and it provided a better means to observe a higher variety of colony morphotypes since the CFUs were dispersed on the 20 plates. Both methods promoted the growth F I G U R E 3 Comparisons of measured and predicted bacterial colony-forming unit counts in dental clinic air samples. Predictions were made by the best-supported NB regression model. Left: Results using the "testing" data; Right: Results using the "training" data.
T A B L E 2 Best-supported NB regression model's performance was evaluated using 300 random training/testing splits of the data set.  (n = 6). All isolates identified as S. hominis underwent whole genome sequencing ( Figure 6). This confirmed their initial species identification.
Culture-based surveillance of the air microbiome may have underestimated species diversity and failed to capture difficult-togrow organisms. While sequence-based approaches such as 16S amplicon sequencing may provide a more complete assessment of the air microbiome, they may also capture remnant nonviable DNA from organisms. As the focus of this study was to number viable aerobic culturable bacteria to evaluate the effectiveness of far-UVC irradiation in reducing their number in future studies, therefore, the culture-based protocols were preferred to culture-independent 16S amplicon sequencing.
This wide range of bacterial species, which was identified from the samplings, showed the diversity of microbial load in the air microbiome over time and the number of patients in the dental clinic. As suggested earlier, this diversity may not only be due to the number of patients but also to variations in patient diagnoses, dental procedures, as well as individual oral bacterial composition (Sato et al., 2015), which differs significantly between individuals.
A wider variety of 50 species over the months of the study were found using passive sampling plates, while only 14 species were isolated using the active sampling method (Table 3). Most bacterial isolates listed in Table 3,  In this study, S. hominis comprised a fraction of 60%-80% of all Staphylococcus species present in the dental clinic air.
F I G U R E 6 Staphylococcus hominis whole genome sequencing (WGS) Dendrogram. It shows four main clusters of S. hominis isolated from active and passive samplings from two locations in VC5 on two distinct dates. Those two dates are 5 months apart from each other, for example, 23 September 2021, and 24 February 2022. S. hominis M1018 was used to make the reference, and S. hominis subsp. hominis M1023 is the Type Strain ATCC 27844 that was used to root the RAxML tree generation. In agreement with this study results, Madsen et al. (2018) also found low or no presence of airborne S. aureus in living rooms or offices in Greater Copenhagen, with S. hominis being the most abundant of the Staphylococcus species present in any room.
Conversely, S. aureus was significantly abundant in samples from nasal cavities or skin from individuals in community households in New York City (Uhlemann et al., 2014). It could be argued that S.
aureus, typically present in nasal cavities and skin, was in low numbers to be captured by this study air sampling, or that S.
hominis from the URT was considerably more abundantly in the air due to patients' respiration. Either way, S. hominis seems to be more abundant in the indoor air microbiome of the dental clinic in this study.
To further characterize the most commonly encountered species, S. hominis, the whole genome sequencing of cultured isolates from the first and last month of this 6-month study was performed. Whole genome sequencing and the dendrogram results are shown in Figure 6. MLST typing identified 13 distinct STs. WGS demonstrated that the isolates were overall highly diverse with a few clusters of closely related isolates. These nearly identical isolates were sampled on the same day except for isolates M1022, M1014, and M1015, sampled 6 months apart.
Additional longitudinal sampling combined with WGS will provide further insights into the ability of long-term persistence of specific clones.
3.3 | Staphylococcus spp. susceptibility to 222 nm-far-UVC and clonality of S. hominis Since S. hominis and S. epidermidis were the most abundant species of Staphylococcus in this study, their susceptibility to 222 nm-far-UVC light was determined in laboratory experiments described above in Section 2.6.
This study results have shown that S. hominis, the most abundant Staphylococcus sp. in this dental clinic, seems to have a similar susceptibility to 222 nm-far-UVC light in laboratory experiments to S. epidermidis, also isolated in the dental clinic in this study. When the results of the testing were fit to a log-linear (exponential) disinfection, the rate constants were determined to be k = 0.475 cm 2 /mJ ± 0.025 (standard error) for S. hominis and k = 0.502 cm 2 /mJ ± 0.018 for S. epidermidis (Figure 7). The results suggest that these Staphylococcus spp. are similarly susceptible to killing by 222 nm-far-UVC. The k values for S. hominis and S. epidermidis in this study are similar to those previously reported in the literature for S. aureus using a KrCl lamp, although without the addition of a filter, which measured k values in liquid suspension to be between 0.62 and 1.12 cm 2 /mJ (Matafonova et al., 2008). These three S. hominis isolates, M1018, M1020, and M1022, have similar susceptibility to 222nm-far-UVC, that is, their DNA repair pathways are similar. Conversely, they represent different clones, as shown in Figure 6, and they may also differ in pathogenicity (not evaluated in this study). is successful in inactivating pathogens in airborne aerosols (Buonanno et al., 2020;Eadie et al., 2022;Welch et al., 2018Welch et al., , 2022. Conversely, in this study, the focus is a dental clinic, a real environment where variables and conditions are uncontrolled.
One of the future challenges is to incorporate a realistic assessment of the reduction in numbers of viable aerobic bacteria in the air microbiome and their susceptibility to far-UVC irradiation while patients are receiving dental treatment.

Regression Modelling and Statistical Analysis
The data collected from air sampling were compiled and imported into R 4.2.0 software for regression analysis (R Core Team, 2017). The dependent variable (outcome) was bacteria_CFUs (colony counts per sample). The sampling time was included in the modeling as the variable "hour", but this variable was later dropped because it did not reach statistical significance.
The independent (predictor) variables were sampling (active or passive), month (calendar month of samplings), weekday (Tuesday or Thursday), hour (hour of samplings, 1, 2, or 3), location (on top of cabinet A or B) and people (average number of people in the room).
The goal of the analysis was to construct a model for predicting the outcome variable (bacterial abundance) using the predictor variables.
The data set was randomly split into training and testing halves.
Model fitting and selection was performed on the training half, and evaluation of performance using a coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) metrics was done on the testing half. Since the outcome data are integer counts, Poisson and Negative Binomial (NB) regressions were considered as the analysis methods. Poisson regression was performed using the glm R function, and NB regression was performed using the glm.nb function in the MASS R package. NB regression outperformed Poisson regression based on Akaike information criterion (AIC) values (Burnham & Anderson, 2002), so it was used for more detailed analyses.
The NB regression utilized here is a generalized linear model (GLM), where the predicted mean outcome was assumed to depend linearly on each predictor variable. However, a log link function was used, so the model is fitted on a natural log scale, i.e., the predictions were ln[mean] and the model parameters (fitted coefficients) were on the ln scale as well. The effect of each predictor was therefore additive on the log scale, but multiplicative on the linear scale.
Model selection was performed on NB regression variants to identify the best-supported model. The initial NB regression attempt on the training data included all relevant predictors: sampling, location, month, weekday, hour, and people. To obtain the best-supported and simplified model, non-significant predictor variables were sequentially removed and resulting model variants were compared using AIC values and likelihood ratio tests (Burnham & Anderson, 2002). We tested for potential multicollinearity between predictor variables using variance inflation factors (VIF) using the VIF R function in package regclass. Potential interactions between the remaining variables were also evaluated.